Spatiotemporal Epidemiological Modeler Research Articles

Evaluation of the Spatiotemporal Epidemiological Modeler (STEM) during the recent COVID-19 pandemic

In early December 2019, some people in China were diagnosed with an unknown pneumonia in Wuhan, in the Hubei province. The responsible of the outbreak was identified in a novel human-infecting coronavirus which differs both from severe acute respiratory syndrome coronavirus and from Middle East respiratory syndrome coronavirus. The new coronavirus, officially named severe acute respiratory syndrome coronavirus 2 by the International Committee on Taxonomy of Viruses, has spread worldwide within few weeks. Only two vaccines have been approved by regulatory agencies and some others are under development. Moreover, effective treatments have not been yet identified or developed even if some potential molecules are under investigation. In a pandemic outbreak, when treatments are not available, the only method that contribute to reduce the virus spreading is the adoption of social distancing measures, like quarantine and isolation. With the intention of better managing emergencies like this, which are a great public health threat, it is important to dispose of predictive epidemiological tools that can help to understand both the virus spreading in terms of people infected, hospitalized, dead and recovered and the effectiveness of containment measures.

Germs on a Plane: The Transmission and Risks of Airplane-Borne Diseases.

This research explored the role of air travel in the spread of infectious diseases, specifically severe acute respiratory syndrome (SARS), H1N1, Ebola, and pneumonic plague. Air travel provides the means for such diseases to spread internationally at extraordinary rates because infected passengers jump from coast to coast and continent to continent within hours. Outbreaks of diseases that spread from person to person test the effectiveness of current public health responses. This research used a mixed methods approach, including use of the Spatiotemporal Epidemiological Modeler, to model the spread of diseases, evaluate the impact of air travel on disease spread, and analyze the effectiveness of different public health strategies and travel policies. Modeling showed that the spread of Ebola and pneumonic plague is minimal and should not be a major air travel concern if an individual becomes infected. H1N1 and SARS have higher infection rates and air travel will facilitate the spread of disease nationally and internationally. To contain the spread of infectious diseases, aviation and public health authorities should establish tailored preventive measures at airports, capture contact information for ticketed passengers, expand the definition of “close contact,” and conduct widespread educational programs. The measures will put in place a foundation for containing the spread of infectious diseases via air travel and minimize the panic and economic consequences that may occur during an outbreak.

Simulation of the use of Yersinia pestis as a Biological Weapon in Nigeria

A simulation study using the Spatiotemporal Epidemiological Modeler was carried out on the potential usage of Yersinia pestis, the causative agent of plague (‘black death’), as a biological weapon of terror. The study revealed widespread infections, incidences and deaths due to the infection all over Nigeria with bioweapon attacks originating from 2 Nigerian cities. Instituting an effective intervention program against the infection could save as many as 3.6 million lives within 10 days of the onset of the intervention program. Intervention programs could include social distancing policies and the use of antibiotics in addition to controlling the rodents and fleas vector population. Because of the relative ease of development of bioweapons and the desperation by terrorists to use any weapon at their disposal to achieve terror, there is an urgent need for an effective preparedness plan that can stop or limit the use of this category A bio-agent for biowarfare.

A Simulation Study on Hypothetical Ebola Virus Transmission in India Using Spatiotemporal Epidemiological Modeler (STEM): A Way towards Precision Public Health

Background. Precision public health is a state-of-the-art concept in public health research and its application in health care. Application of information technology in field of epidemiology paves the way to its transformation to digital epidemiology. A geospatial epidemiological model was simulated to estimate the spread of Ebola virus disease after a hypothetical outbreak in India. Methods. It was a simulation study based on SEIR (Susceptible-Exposed-Infectious-Recovered) compartmental model. Simulation was done in Spatiotemporal Epidemiological Modeler (STEM). Epidemiological profile of Ebola virus, that transmitted throughout the Sierra Leon in 2014–2016, was fitted into the SEIR deterministic compartment model designed for India. Result. Spatiotemporal distribution of EVD exposed, infectious, and recovered population at 4-month interval represented by different figures. It is estimated that if no intervention is taken to stop the spread, within 2 years, almost half of the country will be effected by EVD and cumulative number of exposed individuals, infectious persons, and deaths will be 106947760, 30651674, and 18391005, respectively. Conclusion. Precision public health may play the key role to achieve the health related targets in the Sustainable Development Goals. Policy makers, public health specialists, and data scientists need to put their hands together to make precision public health a reality.

Testing the accuracy ratio of the Spatio-Temporal Epidemiological Modeler (STEM) through Ebola haemorrhagic fever outbreaks.

Mathematical modelling is an important tool for understanding the dynamics of the spread of infectious diseases, which could be the result of a natural outbreak or of the intentional release of pathogenic biological agents. Decision makers and policymakers responsible for strategies to contain disease, prevent epidemics and fight possible bioterrorism attacks, need accurate computational tools, based on mathematical modelling, for preventing or even managing these complex situations. In this article, we tested the validity, and demonstrate the reliability, of an open-source software, the Spatio-Temporal Epidemiological Modeler (STEM), designed to help scientists and public health officials to evaluate and create models of emerging infectious diseases, analysing three real cases of Ebola haemorrhagic fever (EHF) outbreaks: Uganda (2000), Gabon (2001) and Guinea (2014). We discuss the cases analysed through the simulation results obtained with STEM in order to demonstrate the capability of this software in helping decision makers plan interventions in case of biological emergencies.

A Generic Open-Source Software Framework Supporting Scenario Simulations in Bioterrorist Crises

Since the 2001 anthrax attack in the United States, awareness of threats originating from bioterrorism has grown. This led internationally to increased research efforts to improve knowledge of and approaches to protecting human and animal populations against the threat from such attacks. A collaborative effort in this context is the extension of the open-source Spatiotemporal Epidemiological Modeler (STEM) simulation and modeling software for agro- or bioterrorist crisis scenarios. STEM, originally designed to enable community-driven public health disease models and simulations, was extended with new features that enable integration of proprietary data as well as visualization of agent spread along supply and production chains. STEM now provides a fully developed open-source software infrastructure supporting critical modeling tasks such as ad hoc model generation, parameter estimation, simulation of scenario evolution, estimation of effects of mitigation or management measures, and documentation. This open-source software resource can be used free of charge. Additionally, STEM provides critical features like built-in worldwide data on administrative boundaries, transportation networks, or environmental conditions (eg, rainfall, temperature, elevation, vegetation). Users can easily combine their own confidential data with built-in public data to create customized models of desired resolution. STEM also supports collaborative and joint efforts in crisis situations by extended import and export functionalities. In this article we demonstrate specifically those new software features implemented to accomplish STEM application in agro- or bioterrorist crisis scenarios.

Comparing three basic models for seasonal influenza

In this paper we report the use of the open source Spatiotemporal Epidemiological Modeler (STEM, www.eclipse.org/stem) to compare three basic models for seasonal influenza transmission. The models are designed to test for possible differences between the seasonal transmission of influenza A and B. Model 1 assumes that the seasonality and magnitude of transmission do not vary between influenza A and B. Model 2 assumes that the magnitude of seasonal forcing (i.e., the maximum transmissibility), but not the background transmission or flu season length, differs between influenza A and B. Model 3 assumes that the magnitude of seasonal forcing, the background transmission, and flu season length all differ between strains. The models are all optimized using 10 years of surveillance data from 49 of 50 administrative divisions in Israel. Using a cross-validation technique, we compare the relative accuracy of the models and discuss the potential for prediction. We find that accounting for variation in transmission amplitude increases the predictive ability compared to the base. However, little improvement is obtained by allowing for further variation in the shape of the seasonal forcing function.

Infectious Disease Modeling: Creating a Community to Respond to Biological Threats

The rise of global economies in the 21st century, the rapid national and international movement of people, and the increased reliance of developed countries on global trade, all greatly increase the potential and possible magnitude of a worldwide pandemic. New epidemics may be the result of global climate change, vector-borne diseases, food-borne illness, new naturally occurring pathogens, or bio-terrorist attacks. The threat is most severe for highly communicable diseases. When rapidly spreading microparasitic infections coincide with the rapid transportation, propagation, and dissemination of the pathogens and vectors for infection, the risks associated with emerging infectious disease increase. We discuss the use of publicly-available technologies in assisting public health officials and scientists in protecting populations from emerging disease or in implementing improved response measures. We illustrate possibilities using the SpatioTemporal Epidemiological Modeler (STEM) that was developed to run on the Open Health Framework (OHF) created by the Eclipse Foundation in 2004. An illustration regarding the spread of the influenza H1N1 virus from Mexico to the United States via air travel in Spring 2009 is briefly discussed.

Epidemic Simulation for Syndromic Surveillance

This article reports on a project to develop a simulation-based test bed for the BioDefend Syndromic Surveillance System. BioDefend is a system that data mines syndrome reports from emergency rooms and so forth to produce early alerts of epidemic onset. An existing large-scale epidemic simulation will be adapted to provide synthetic reports of syndromes associated with extremely rare events such as pandemics and bioterrorism. The Spatiotemporal Epidemiological Modeler will be used as the basis of the test bed. Results from the much simpler Spatiotemporal Epidemiological Modeler simulation will be validated by comparison against results from the more complex Epidemiological Simulation System. These synthesized reports will be used to test BioDefend's ability to detect epidemic outbreaks and to evaluate its data-mining algorithm. The development of an optimal algorithm for processing syndrome reports to provide reliable epidemic early warnings is a difficult research problem that the test bed should help address.